Data Science Workbench by kanishak kaushal.pptx

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1. B.TECH (COMPUTER SCIENCE AND ENGINEERING) 7 th SEM. HIMACHAL PRADESH TECHNICAL UNIVERSITY, HAMIRPUR SUBMITTED BY: SUBMITTED TO: KANISHAK MS. SUNITA DHIMAN 22010603034 TOPIC – DATA SCIENCE WORKBENCH SUBJECT -PROJECT WORK

2. Data Science Workbench An End-to-End Analytics Platform for Students Start here

3. Introduction The Data Science Workbench is designed to enable students and educators to perform full data science tasks. It integrates tools for exploration, modeling, and prediction using popular Python libraries and interactive web deployment, all in a user-friendly platform.

4. Introduction to Data Science Workbench 01

5. Platform Overview Data Science Workbench offers dataset upload and preview, preprocessing steps, exploratory data analysis (EDA), supervised and unsupervised learning, prediction interface, and model evaluation—all accessible through a streamlined interface.

6. Key Features Includes handling missing values, encoding, scaling, correlation heatmaps, various modeling algorithms like Random Forest and SVM, model evaluation with confusion matrices, and export options for datasets and visuals.

7. Target User: Students Designed for students to learn hands-on data science with an easy interface suitable for non-coders. Useful for lab sessions, assignments, projects, and to build practical data analytics skills.

8. Analytics and Tools 02

9. Data Import and Cleaning Supports uploading datasets with tools to handle missing values, convert categorical data, and apply feature scaling, ensuring clean data ready for analysis and modeling.

10. Visualization Techniques Provides descriptive statistics, correlation heatmaps, pairplots, boxplots, histograms, and other interactive visualizations using libraries like seaborn, matplotlib, and plotly.

11. Model Building and Evaluation The platform supports supervised learning algorithms like Random Forest, Logistic Regression, Gradient Boosting, and SVM. It provides tools for classification reports, confusion matrices, cross-validation, and real-time progress tracking during training. Users can reuse trained models for new predictions easily.

12. Interactive Visual Components 03

13. Heatmaps for Data Insight Correlation heatmaps visualize relationships among variables clearly. They help identify strong and weak correlations and guide feature selection. Heatmaps are interactive with color gradients for quick data pattern recognition.

14. Confusion Matrices for Model Performance Confusion matrices provide detailed error analysis of classifiers. They display true positives, false positives, true negatives, and false negatives. This helps students understand model strengths, weaknesses, and improve accuracy.

15. Code-Inspired Layouts for User Interface The UI uses a streamlined sidebar and tab system inspired by code editors. It offers clean, minimal blue-themed design and easy navigation. Layouts balance interactive visuals and code-like panels for an intuitive experience.

16. Platform Benefits and Use Cases 04

17. Enhancing Learning Experience The platform offers hands-on data science practice with minimal setup. It supports a variety of tasks from data cleaning to model evaluation. Designed to engage students in practical, project-based learning.

18. Supporting Academic Projects Students can use the platform for assignments, labs, and research projects. Tools enable thorough exploration, analysis, and reporting in one place. Exports support sharing results and including visuals in reports.

19. Preparing for Industry Challenges The Workbench introduces students to professional-grade tools. Supports building skills in data preprocessing, modeling, and evaluation. Prepares students for real-world data science workflows and collaboration.

20. Conclusions Data Science Workbench is an all-in-one platform tailored for students. It simplifies complex data science processes with intuitive tools and visuals. Designed to boost learning, project work, and readiness for data science careers.